Volume 9, Issue 1 (Spring 2023)
JMIS 2023, 9(1): 46-55 |
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Dehghani M, Rahimi B, Makhdomi K, Jebraeily M. Determining Key Performance Indicators in Dialysis Management Dashboard for Monitoring Service Quality. JMIS 2023; 9 (1) :46-55
URL: http://jmis.hums.ac.ir/article-1-431-en.html
URL: http://jmis.hums.ac.ir/article-1-431-en.html
Department of Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran.
Abstract: (1888 Views)
Objective Considering the impact of service quality in the hemodialysis departments on the survival rate and quality of life (QoL) of patients, it is important to monitor the performance of this department. In this regard, identifying key performance indicators (KPIs) can be useful. This study aims to determine the k KPIs in the dialysis management dashboard.
Methods This is a descriptive cross-sectional study that was conducted in 2022. Participants were 346 nephrology specialists in Iran which were selected using the census method. The data collection tool was a researcher-made questionnaire with acceptable validity and reliability. Data were analyzed using SPSS software, version 16.
Results A total of 40 KPIs were identified in three main categories: Structure (n=16), process (n=11), and outcome (n=13). The most important structure-related KPIs were: Underlying cause of kidney failure, comorbidity diseases, blood type, history of viral diseases, dry weight, and history of kidney transplantation. The most important process-related KPIs were: Type of filtration, type of vascular access, and duration of dialysis. The most important outcome-related KPIs were: Kt/V, urea reduction ratio, normalized protein catabolic rate, and fistula infection.
Conclusion Accurate identification of KPIs and their use in continuous monitoring of dialysis department performance can help improve service quality and patient safety.
Methods This is a descriptive cross-sectional study that was conducted in 2022. Participants were 346 nephrology specialists in Iran which were selected using the census method. The data collection tool was a researcher-made questionnaire with acceptable validity and reliability. Data were analyzed using SPSS software, version 16.
Results A total of 40 KPIs were identified in three main categories: Structure (n=16), process (n=11), and outcome (n=13). The most important structure-related KPIs were: Underlying cause of kidney failure, comorbidity diseases, blood type, history of viral diseases, dry weight, and history of kidney transplantation. The most important process-related KPIs were: Type of filtration, type of vascular access, and duration of dialysis. The most important outcome-related KPIs were: Kt/V, urea reduction ratio, normalized protein catabolic rate, and fistula infection.
Conclusion Accurate identification of KPIs and their use in continuous monitoring of dialysis department performance can help improve service quality and patient safety.
Type of Study: Research |
Subject:
Special
Received: 2022/11/1 | Accepted: 2022/12/17 | Published: 2023/05/31
Received: 2022/11/1 | Accepted: 2022/12/17 | Published: 2023/05/31
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